AIMC Topic: Algorithms

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Attentive transformer deep learning algorithm for intrusion detection on IoT systems using automatic Xplainable feature selection.

PloS one
Recent years have witnessed an in-depth proliferation of the Internet of Things (IoT) and Industrial Internet of Things (IIoT) systems linked to Industry 4.0 technology. The increasing rate of IoT device usage is associated with rising security risks...

Embryo ranking agreement between embryologists and artificial intelligence algorithms.

F&S science
OBJECTIVE: To evaluate the degree of agreement of embryo ranking between embryologists and eight artificial intelligence (AI) algorithms.

DeepADRA2A: predicting adrenergic α2a inhibitors using deep learning.

Journal of biomolecular structure & dynamics
Adrenergic α2a (ADRA2A) receptors play a crucial role in modulating various physiological actions, thereby influencing the proper functioning of different systems in the body. ADRA2A regulation is associated with a wide range of effects, including al...

AC-PLT: An algorithm for computer-assisted coding of semantic property listing data.

Behavior research methods
In this paper, we present a novel algorithm that uses machine learning and natural language processing techniques to facilitate the coding of feature listing data. Feature listing is a method in which participants are asked to provide a list of featu...

Deep Learning-Based Classification of Epileptic Electroencephalography Signals Using a Concentrated Time-Frequency Approach.

International journal of neural systems
ConceFT (concentration of frequency and time) is a new time-frequency (TF) analysis method which combines multitaper technique and synchrosqueezing transform (SST). This combination produces highly concentrated TF representations with approximately p...

Deep graph convolutional network for small-molecule retention time prediction.

Journal of chromatography. A
The retention time (RT) is a crucial source of data for liquid chromatography-mass spectrometry (LCMS). A model that can accurately predict the RT for each molecule would empower filtering candidates with similar spectra but differing RT in LCMS-base...

FAFuse: A Four-Axis Fusion framework of CNN and Transformer for medical image segmentation.

Computers in biology and medicine
Medical image segmentation is crucial for accurate diagnosis and treatment in the medical field. In recent years, convolutional neural networks (CNNs) and Transformers have been frequently adopted as network architectures in medical image segmentatio...

Deep Learning Algorithms to Detect Murmurs Associated With Structural Heart Disease.

Journal of the American Heart Association
Background The success of cardiac auscultation varies widely among medical professionals, which can lead to missed treatments for structural heart disease. Applying machine learning to cardiac auscultation could address this problem, but despite rece...

Negation recognition in clinical natural language processing using a combination of the NegEx algorithm and a convolutional neural network.

BMC medical informatics and decision making
BACKGROUND: Important clinical information of patients is present in unstructured free-text fields of Electronic Health Records (EHRs). While this information can be extracted using clinical Natural Language Processing (cNLP), the recognition of nega...

Exploring the performance of implicit neural representations for brain image registration.

Scientific reports
Pairwise image registration is a necessary prerequisite for brain image comparison and data integration in neuroscience and radiology. In this work, we explore the efficacy of implicit neural representations (INRs) in improving the performance of bra...